Abstract Identifying the biological features of the human immune response that correlate with and predict the development of an effective immune response to vaccination is an overarching goal of this U19 consortium. In this core we will apply a suite of computational tools to analyze data from human samples of serum, peripheral blood mononuclear cell (PBMC), and individual immune cells obtained before and after vaccination to create new knowledge about the biological basis for effective vaccine-mediated immunity. Although these datasets will primarily be gene expression profiles of PBMC and single immune cells, we will also integrate data from analysis of serum metabolite abundance. To achieve these two goals, we have assembled a team of computational biologists and immunologists with deep expertise in the generation and analysis of highly complex datasets of transcript abundance and metabolic profiles, who will support Projects 1 and 2 in the following aims: Aim 1. Identify knowledge-based molecular signatures that predict vaccine immunogenicity. A common theme of the proposed analytic approaches is that they use ?knowledge-based? approaches to identifying features the correlate with vaccination. These knowledge-based approaches have been developed as a result of a growing appreciation that analyses that focus on individual, unrelated genes that correlate with vaccine outcome often fail to offer mechanistic understanding of how that vaccine elicits immunity. Instead, we will use gene-set based analysis that leverage compendia of immune signatures generated in the previous funding period. Aim 2: Resolve cellular heterogeneity in the innate and adaptive vaccine response to the single cell level. Immunologic protection represents the combined activity of dozens of phenotypically and functionally distinct cell types. Although much can be learned from the analysis of profiles of aggregates of cells (such as expression profiles of PBMC), measurements in bulk populations of cells masks the existence and function of individual cells. This aim will provide the tools to generate, analyze and interpret global gene expression profiles from individual immune cells using single cell RNA-seq. Aim 3: Develop integrative models of vaccine immunity from orthogonal data sources. One of the principal challenges to understanding the immune response to vaccination is the complexity of the system itself. Thus the challenge of understanding the complexity of the immune response to vaccine lies not only in mapping the range of cellular phenotypes, that change following vaccination, but in integrating different ?views? of the immune response. In this aim we will apply integrative modeling approaches to link metabolomic and gene expression profiles in vaccinated subjects.